Overview

Dataset statistics

Number of variables14
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory109.5 KiB
Average record size in memory112.1 B

Variable types

Numeric12
Categorical2

Alerts

CLASS is highly overall correlated with HbA1cHigh correlation
Cr is highly overall correlated with UreaHigh correlation
HbA1c is highly overall correlated with CLASSHigh correlation
TG is highly overall correlated with VLDLHigh correlation
Urea is highly overall correlated with CrHigh correlation
VLDL is highly overall correlated with TGHigh correlation
CLASS is highly imbalanced (65.0%)Imbalance

Reproduction

Analysis started2024-02-15 05:52:57.595843
Analysis finished2024-02-15 05:53:40.947839
Duration43.35 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

ID
Real number (ℝ)

Distinct800
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean340.5
Minimum1
Maximum800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-02-15T05:53:41.119411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25.95
Q1125.75
median300.5
Q3550.25
95-th percentile750.05
Maximum800
Range799
Interquartile range (IQR)424.5

Descriptive statistics

Standard deviation240.39767
Coefficient of variation (CV)0.70601372
Kurtosis-1.2222318
Mean340.5
Median Absolute Deviation (MAD)200
Skewness0.33267921
Sum340500
Variance57791.041
MonotonicityNot monotonic
2024-02-15T05:53:41.435918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76 2
 
0.2%
108 2
 
0.2%
57 2
 
0.2%
26 2
 
0.2%
69 2
 
0.2%
117 2
 
0.2%
44 2
 
0.2%
87 2
 
0.2%
155 2
 
0.2%
80 2
 
0.2%
Other values (790) 980
98.0%
ValueCountFrequency (%)
1 2
0.2%
2 2
0.2%
3 2
0.2%
4 2
0.2%
5 2
0.2%
6 2
0.2%
7 2
0.2%
8 2
0.2%
9 2
0.2%
10 2
0.2%
ValueCountFrequency (%)
800 1
0.1%
799 1
0.1%
798 1
0.1%
797 1
0.1%
796 1
0.1%
795 1
0.1%
794 1
0.1%
793 1
0.1%
792 1
0.1%
791 1
0.1%

No_Pation
Real number (ℝ)

Distinct961
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean270551.41
Minimum123
Maximum75435657
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-02-15T05:53:41.743386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum123
5-th percentile3024.8
Q124063.75
median34395.5
Q345384.25
95-th percentile454316
Maximum75435657
Range75435534
Interquartile range (IQR)21320.5

Descriptive statistics

Standard deviation3380757.8
Coefficient of variation (CV)12.495806
Kurtosis400.76331
Mean270551.41
Median Absolute Deviation (MAD)10359
Skewness19.561029
Sum2.7055141 × 108
Variance1.1429523 × 1013
MonotonicityNot monotonic
2024-02-15T05:53:42.217365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
454316 19
 
1.9%
856 2
 
0.2%
87654 2
 
0.2%
71741 2
 
0.2%
34290 2
 
0.2%
14389 2
 
0.2%
34517 2
 
0.2%
48362 2
 
0.2%
45646 2
 
0.2%
44835 2
 
0.2%
Other values (951) 963
96.3%
ValueCountFrequency (%)
123 1
0.1%
215 1
0.1%
234 2
0.2%
244 1
0.1%
252 1
0.1%
298 1
0.1%
345 2
0.2%
355 1
0.1%
356 1
0.1%
451 1
0.1%
ValueCountFrequency (%)
75435657 1
0.1%
66467878 1
0.1%
33656789 1
0.1%
8785782 1
0.1%
8432454 1
0.1%
7565435 1
0.1%
4515131 1
0.1%
1036556 1
0.1%
985322 1
0.1%
905146 1
0.1%

Gender
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
M
565 
F
434 
f
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowF
2nd rowM
3rd rowF
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
M 565
56.5%
F 434
43.4%
f 1
 
0.1%

Length

2024-02-15T05:53:42.716004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-15T05:53:43.173630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
m 565
56.5%
f 435
43.5%

Most occurring characters

ValueCountFrequency (%)
M 565
56.5%
F 434
43.4%
f 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 999
99.9%
Lowercase Letter 1
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 565
56.6%
F 434
43.4%
Lowercase Letter
ValueCountFrequency (%)
f 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 565
56.5%
F 434
43.4%
f 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 565
56.5%
F 434
43.4%
f 1
 
0.1%

AGE
Real number (ℝ)

Distinct50
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.528
Minimum20
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-02-15T05:53:43.551717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile33
Q151
median55
Q359
95-th percentile66
Maximum79
Range59
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.799241
Coefficient of variation (CV)0.16438576
Kurtosis1.4356393
Mean53.528
Median Absolute Deviation (MAD)4
Skewness-0.8195359
Sum53528
Variance77.426643
MonotonicityNot monotonic
2024-02-15T05:53:43.978244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55 181
18.1%
60 88
 
8.8%
54 84
 
8.4%
51 51
 
5.1%
61 49
 
4.9%
56 48
 
4.8%
52 44
 
4.4%
50 43
 
4.3%
59 32
 
3.2%
57 31
 
3.1%
Other values (40) 349
34.9%
ValueCountFrequency (%)
20 1
 
0.1%
25 1
 
0.1%
26 2
 
0.2%
28 3
 
0.3%
30 20
2.0%
31 9
0.9%
32 1
 
0.1%
33 16
1.6%
34 5
 
0.5%
35 11
1.1%
ValueCountFrequency (%)
79 1
 
0.1%
77 4
0.4%
76 4
0.4%
75 2
 
0.2%
73 8
0.8%
71 1
 
0.1%
70 2
 
0.2%
69 6
0.6%
68 8
0.8%
67 6
0.6%

Urea
Real number (ℝ)

HIGH CORRELATION 

Distinct110
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.124743
Minimum0.5
Maximum38.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-02-15T05:53:44.440895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile2.3
Q13.7
median4.6
Q35.7
95-th percentile9.615
Maximum38.9
Range38.4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.9351654
Coefficient of variation (CV)0.57274393
Kurtosis30.427642
Mean5.124743
Median Absolute Deviation (MAD)1
Skewness4.2989279
Sum5124.743
Variance8.6151962
MonotonicityNot monotonic
2024-02-15T05:53:44.749652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.3 40
 
4.0%
4 37
 
3.7%
5 36
 
3.6%
4.8 34
 
3.4%
4.1 33
 
3.3%
3 32
 
3.2%
4.7 28
 
2.8%
4.6 26
 
2.6%
4.2 24
 
2.4%
3.8 23
 
2.3%
Other values (100) 687
68.7%
ValueCountFrequency (%)
0.5 1
 
0.1%
1.1 1
 
0.1%
1.2 2
 
0.2%
1.8 3
 
0.3%
1.9 1
 
0.1%
2 19
1.9%
2.1 16
1.6%
2.2 5
 
0.5%
2.3 9
0.9%
2.4 5
 
0.5%
ValueCountFrequency (%)
38.9 1
 
0.1%
26.4 1
 
0.1%
24 3
0.3%
22 2
0.2%
20.8 4
0.4%
20 1
 
0.1%
17.1 1
 
0.1%
14.9 1
 
0.1%
14.5 2
0.2%
14.1 2
0.2%

Cr
Real number (ℝ)

HIGH CORRELATION 

Distinct113
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.943
Minimum6
Maximum800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-02-15T05:53:45.042850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile33.95
Q148
median60
Q373
95-th percentile111
Maximum800
Range794
Interquartile range (IQR)25

Descriptive statistics

Standard deviation59.984747
Coefficient of variation (CV)0.87006291
Kurtosis91.714882
Mean68.943
Median Absolute Deviation (MAD)12
Skewness8.4741512
Sum68943
Variance3598.1699
MonotonicityNot monotonic
2024-02-15T05:53:45.332123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 55
 
5.5%
72 32
 
3.2%
55 30
 
3.0%
63 27
 
2.7%
53 27
 
2.7%
48 26
 
2.6%
70 25
 
2.5%
62 24
 
2.4%
59 23
 
2.3%
38 22
 
2.2%
Other values (103) 709
70.9%
ValueCountFrequency (%)
6 1
 
0.1%
20 2
0.2%
22 2
0.2%
23 4
0.4%
24 2
0.2%
25 2
0.2%
26 1
 
0.1%
27 2
0.2%
28 4
0.4%
29 1
 
0.1%
ValueCountFrequency (%)
800 4
0.4%
401 3
0.3%
370 2
0.2%
344 1
 
0.1%
327 1
 
0.1%
315 2
0.2%
243 1
 
0.1%
230 1
 
0.1%
228 1
 
0.1%
203 1
 
0.1%

HbA1c
Real number (ℝ)

HIGH CORRELATION 

Distinct111
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.28116
Minimum0.9
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-02-15T05:53:45.675842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile4.2
Q16.5
median8
Q310.2
95-th percentile12.405
Maximum16
Range15.1
Interquartile range (IQR)3.7

Descriptive statistics

Standard deviation2.5340031
Coefficient of variation (CV)0.30599616
Kurtosis-0.25086926
Mean8.28116
Median Absolute Deviation (MAD)1.8
Skewness0.2216894
Sum8281.16
Variance6.4211718
MonotonicityNot monotonic
2024-02-15T05:53:45.956644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 34
 
3.4%
9 32
 
3.2%
7 32
 
3.2%
4 30
 
3.0%
6.8 30
 
3.0%
6 25
 
2.5%
10.2 24
 
2.4%
7.2 21
 
2.1%
5 20
 
2.0%
7.7 19
 
1.9%
Other values (101) 733
73.3%
ValueCountFrequency (%)
0.9 4
 
0.4%
2 1
 
0.1%
3 1
 
0.1%
3.7 4
 
0.4%
4 30
3.0%
4.1 8
 
0.8%
4.2 5
 
0.5%
4.3 10
 
1.0%
4.5 7
 
0.7%
4.6 1
 
0.1%
ValueCountFrequency (%)
16 1
 
0.1%
15.9 1
 
0.1%
15 2
0.2%
14.8 1
 
0.1%
14.7 3
0.3%
14.6 2
0.2%
14.5 2
0.2%
14.4 1
 
0.1%
14.1 1
 
0.1%
13.9 3
0.3%

Chol
Real number (ℝ)

Distinct77
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.86282
Minimum0
Maximum10.3
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-02-15T05:53:46.247889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q14
median4.8
Q35.6
95-th percentile7.1
Maximum10.3
Range10.3
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.3017375
Coefficient of variation (CV)0.2676919
Kurtosis1.9243445
Mean4.86282
Median Absolute Deviation (MAD)0.8
Skewness0.61712266
Sum4862.82
Variance1.6945206
MonotonicityNot monotonic
2024-02-15T05:53:46.555549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.4 55
 
5.5%
4.9 46
 
4.6%
4.8 40
 
4.0%
4 38
 
3.8%
5.3 38
 
3.8%
4.2 37
 
3.7%
4.1 36
 
3.6%
3.6 33
 
3.3%
5.2 33
 
3.3%
4.7 30
 
3.0%
Other values (67) 614
61.4%
ValueCountFrequency (%)
0 1
 
0.1%
0.5 1
 
0.1%
0.6 2
 
0.2%
1.2 1
 
0.1%
2 6
0.6%
2.1 2
 
0.2%
2.3 3
0.3%
2.4 6
0.6%
2.5 4
0.4%
2.6 4
0.4%
ValueCountFrequency (%)
10.3 1
 
0.1%
9.9 1
 
0.1%
9.8 2
0.2%
9.7 2
0.2%
9.5 4
0.4%
9.3 1
 
0.1%
9.2 1
 
0.1%
9.1 1
 
0.1%
8.8 2
0.2%
8.6 1
 
0.1%

TG
Real number (ℝ)

HIGH CORRELATION 

Distinct69
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.34961
Minimum0.3
Maximum13.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-02-15T05:53:46.840731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.8
Q11.5
median2
Q32.9
95-th percentile5.1
Maximum13.8
Range13.5
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.401176
Coefficient of variation (CV)0.59634408
Kurtosis10.26366
Mean2.34961
Median Absolute Deviation (MAD)0.7
Skewness2.2984561
Sum2349.61
Variance1.9632942
MonotonicityNot monotonic
2024-02-15T05:53:47.150952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.1 65
 
6.5%
2 61
 
6.1%
1.3 52
 
5.2%
1.5 51
 
5.1%
1.7 44
 
4.4%
1.9 42
 
4.2%
1.8 38
 
3.8%
1.6 35
 
3.5%
2.2 34
 
3.4%
1.2 33
 
3.3%
Other values (59) 545
54.5%
ValueCountFrequency (%)
0.3 2
 
0.2%
0.5 1
 
0.1%
0.6 14
1.4%
0.7 25
2.5%
0.8 17
1.7%
0.9 15
1.5%
1 29
2.9%
1.1 31
3.1%
1.19 3
 
0.3%
1.2 33
3.3%
ValueCountFrequency (%)
13.8 1
 
0.1%
12.7 1
 
0.1%
11.6 1
 
0.1%
8.7 1
 
0.1%
8.5 1
 
0.1%
7.7 2
0.2%
7.2 2
0.2%
7 3
0.3%
6.8 3
0.3%
6.7 2
0.2%

HDL
Real number (ℝ)

Distinct48
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.20475
Minimum0.2
Maximum9.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-02-15T05:53:47.458730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.7
Q10.9
median1.1
Q31.3
95-th percentile1.9
Maximum9.9
Range9.7
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.66041357
Coefficient of variation (CV)0.54817478
Kurtosis62.629947
Mean1.20475
Median Absolute Deviation (MAD)0.2
Skewness6.2832012
Sum1204.75
Variance0.43614608
MonotonicityNot monotonic
2024-02-15T05:53:47.755665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1.1 147
14.7%
0.9 141
14.1%
1 135
13.5%
0.8 84
8.4%
1.3 80
8.0%
1.2 71
7.1%
0.7 57
 
5.7%
1.4 54
 
5.4%
1.6 40
 
4.0%
1.8 31
 
3.1%
Other values (38) 160
16.0%
ValueCountFrequency (%)
0.2 1
 
0.1%
0.4 7
 
0.7%
0.5 7
 
0.7%
0.6 24
 
2.4%
0.7 57
5.7%
0.75 3
 
0.3%
0.8 84
8.4%
0.9 141
14.1%
0.95 1
 
0.1%
1 135
13.5%
ValueCountFrequency (%)
9.9 1
0.1%
9 1
0.1%
6.6 2
0.2%
6.3 1
0.1%
5 1
0.1%
4 1
0.1%
3.9 1
0.1%
3.8 1
0.1%
3.6 2
0.2%
3.4 1
0.1%

LDL
Real number (ℝ)

Distinct65
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.60979
Minimum0.3
Maximum9.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-02-15T05:53:48.045766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile1
Q11.8
median2.5
Q33.3
95-th percentile4.3
Maximum9.9
Range9.6
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.1151017
Coefficient of variation (CV)0.42727643
Kurtosis4.27576
Mean2.60979
Median Absolute Deviation (MAD)0.75
Skewness1.1459096
Sum2609.79
Variance1.2434519
MonotonicityNot monotonic
2024-02-15T05:53:48.341280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.5 87
 
8.7%
2 55
 
5.5%
1.7 40
 
4.0%
1.4 37
 
3.7%
3.1 37
 
3.7%
3 37
 
3.7%
2.6 35
 
3.5%
3.6 32
 
3.2%
3.5 32
 
3.2%
1.3 29
 
2.9%
Other values (55) 579
57.9%
ValueCountFrequency (%)
0.3 1
 
0.1%
0.5 2
 
0.2%
0.6 3
 
0.3%
0.7 2
 
0.2%
0.75 3
 
0.3%
0.8 7
 
0.7%
0.9 22
2.2%
0.95 4
 
0.4%
0.96 1
 
0.1%
1 9
0.9%
ValueCountFrequency (%)
9.9 2
0.2%
7.9 2
0.2%
7.5 1
 
0.1%
7 1
 
0.1%
6.4 1
 
0.1%
5.9 1
 
0.1%
5.6 3
0.3%
5.5 4
0.4%
5.3 1
 
0.1%
5.1 1
 
0.1%

VLDL
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8547
Minimum0.1
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-02-15T05:53:48.647812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.4
Q10.7
median0.9
Q31.5
95-th percentile8.1
Maximum35
Range34.9
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation3.6635993
Coefficient of variation (CV)1.9753056
Kurtosis33.51147
Mean1.8547
Median Absolute Deviation (MAD)0.3
Skewness5.3504447
Sum1854.7
Variance13.42196
MonotonicityNot monotonic
2024-02-15T05:53:48.932449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9 132
13.2%
0.7 94
 
9.4%
0.6 80
 
8.0%
0.8 76
 
7.6%
1 71
 
7.1%
1.5 63
 
6.3%
0.5 55
 
5.5%
1.1 54
 
5.4%
1.3 44
 
4.4%
0.4 37
 
3.7%
Other values (50) 294
29.4%
ValueCountFrequency (%)
0.1 2
 
0.2%
0.2 5
 
0.5%
0.3 26
 
2.6%
0.4 37
 
3.7%
0.5 55
5.5%
0.6 80
8.0%
0.7 94
9.4%
0.8 76
7.6%
0.9 132
13.2%
1 71
7.1%
ValueCountFrequency (%)
35 1
0.1%
33.6 1
0.1%
31.8 2
0.2%
31 1
0.1%
27.2 1
0.1%
24.5 1
0.1%
22.7 1
0.1%
22.2 2
0.2%
19.5 1
0.1%
18.1 1
0.1%

BMI
Real number (ℝ)

Distinct64
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.57802
Minimum19
Maximum47.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-02-15T05:53:49.226993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile21
Q126
median30
Q333
95-th percentile38
Maximum47.75
Range28.75
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.9623881
Coefficient of variation (CV)0.16777283
Kurtosis-0.29614367
Mean29.57802
Median Absolute Deviation (MAD)3
Skewness0.12580694
Sum29578.02
Variance24.625296
MonotonicityNot monotonic
2024-02-15T05:53:49.514980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 114
 
11.4%
33 101
 
10.1%
29 72
 
7.2%
26 64
 
6.4%
24 58
 
5.8%
28 56
 
5.6%
31 53
 
5.3%
27 46
 
4.6%
32 45
 
4.5%
21 36
 
3.6%
Other values (54) 355
35.5%
ValueCountFrequency (%)
19 7
 
0.7%
19.5 1
 
0.1%
20 10
 
1.0%
21 36
3.6%
21.17 2
 
0.2%
22 36
3.6%
22.5 1
 
0.1%
23 35
3.5%
23.5 1
 
0.1%
24 58
5.8%
ValueCountFrequency (%)
47.75 1
 
0.1%
47 2
 
0.2%
43.25 2
 
0.2%
40.5 2
 
0.2%
40 2
 
0.2%
39.18 2
 
0.2%
39 22
2.2%
38.62 1
 
0.1%
38 24
2.4%
37.62 1
 
0.1%

CLASS
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Y
840 
N
102 
P
 
53
Y
 
4
N
 
1

Length

Max length2
Median length1
Mean length1.005
Min length1

Characters and Unicode

Total characters1005
Distinct characters4
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
Y 840
84.0%
N 102
 
10.2%
P 53
 
5.3%
Y 4
 
0.4%
N 1
 
0.1%

Length

2024-02-15T05:53:49.802864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-15T05:53:50.049363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
y 844
84.4%
n 103
 
10.3%
p 53
 
5.3%

Most occurring characters

ValueCountFrequency (%)
Y 844
84.0%
N 103
 
10.2%
P 53
 
5.3%
5
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1000
99.5%
Space Separator 5
 
0.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Y 844
84.4%
N 103
 
10.3%
P 53
 
5.3%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1000
99.5%
Common 5
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y 844
84.4%
N 103
 
10.3%
P 53
 
5.3%
Common
ValueCountFrequency (%)
5
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1005
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y 844
84.0%
N 103
 
10.2%
P 53
 
5.3%
5
 
0.5%

Interactions

2024-02-15T05:53:37.008771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:52:58.168961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:01.747746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:05.499169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:08.940217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:12.419032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:15.645053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:19.672883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:23.091163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:26.252746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:29.473259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:33.339413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:37.271632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:52:58.450618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:02.014024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:05.904619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:09.212058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:12.681783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:15.915304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:19.942206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:23.369952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:26.523899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:29.911462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:33.613948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:37.538493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:52:58.722559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:02.283021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:06.281253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:09.485193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:12.966243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:16.193513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:20.198893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:23.628620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:26.776243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:30.311082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:33.880364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:37.791018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:52:58.992032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:02.556512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:06.573171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:09.755846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:13.223352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:16.483287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:20.470812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:23.891017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:27.039332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:30.712127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:34.140623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:38.075947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:52:59.256914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:02.843806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:06.852993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:10.026340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:13.504667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:16.898646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:20.734366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:24.160484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:27.299332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:31.104667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:34.400792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:38.331824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:52:59.543933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:03.119329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:07.116181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:10.286256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:13.783479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:17.325380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:20.992490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:24.440470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:27.585195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:31.449760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:34.676332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:38.597128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:52:59.824818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:03.371115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:07.382352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:10.876792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:14.068442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:17.679154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:21.236824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:24.714435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:27.845157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:31.820477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:34.961074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:38.835708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:00.078802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:03.669241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:07.631651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:11.132188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:14.325938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:18.055092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:21.499414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:24.958938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:28.097358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:32.062376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:35.209518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:39.109061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:00.346673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:04.082512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:07.908664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:11.390350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:14.585324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:18.427498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:21.757458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:25.215988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:28.347315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:32.333722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:35.478968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:39.355388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:00.962111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:04.413869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:08.158643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:11.640623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:14.845227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:18.774819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:21.997300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:25.473834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:28.610737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:32.581675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:35.735270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:39.614355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:01.204980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:04.758941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:08.415739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:11.891262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:15.113572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:19.154361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:22.613378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:25.723939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:28.846373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:32.840327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:36.003581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:39.870063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:01.469196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:05.118870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:08.684515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:12.166926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:15.382148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:19.415201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:22.861754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:25.990391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:29.111220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:33.100960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T05:53:36.276450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-02-15T05:53:50.263662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
AGEBMICLASSCholCrGenderHDLHbA1cIDLDLNo_PationTGUreaVLDL
AGE1.0000.3730.2890.0390.0610.071-0.0520.411-0.0650.001-0.0600.1750.1550.036
BMI0.3731.0000.3980.0060.0370.1200.0370.4170.046-0.115-0.0400.1070.0520.214
CLASS0.2890.3981.0000.1720.0480.087-0.0130.568-0.055-0.008-0.0900.2260.0920.235
Chol0.0390.0060.1721.000-0.0040.0000.1090.1520.0330.4320.1060.3450.0040.251
Cr0.0610.0370.048-0.0041.0000.000-0.069-0.082-0.0730.0850.0710.0790.5680.097
Gender0.0710.1200.0870.0000.0001.000-0.1190.0060.0210.0440.0660.0160.1860.074
HDL-0.0520.037-0.0130.109-0.069-0.1191.000-0.027-0.026-0.191-0.006-0.165-0.054-0.069
HbA1c0.4110.4170.5680.152-0.0820.006-0.0271.000-0.011-0.0230.0490.2320.0150.222
ID-0.0650.046-0.0550.033-0.0730.021-0.026-0.0111.000-0.0610.037-0.094-0.0570.034
LDL0.001-0.115-0.0080.4320.0850.044-0.191-0.023-0.0611.0000.0510.063-0.0130.020
No_Pation-0.060-0.040-0.0900.1060.0710.066-0.0060.0490.0370.0511.0000.0410.0360.139
TG0.1750.1070.2260.3450.0790.016-0.1650.232-0.0940.0630.0411.0000.0700.599
Urea0.1550.0520.0920.0040.5680.186-0.0540.015-0.057-0.0130.0360.0701.0000.016
VLDL0.0360.2140.2350.2510.0970.074-0.0690.2220.0340.0200.1390.5990.0161.000

Missing values

2024-02-15T05:53:40.253381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-15T05:53:40.749983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDNo_PationGenderAGEUreaCrHbA1cCholTGHDLLDLVLDLBMICLASS
050217975F504.7464.94.20.92.41.40.524.0N
173534221M264.5624.93.71.41.12.10.623.0N
242047975F504.7464.94.20.92.41.40.524.0N
368087656F504.7464.94.20.92.41.40.524.0N
450434223M337.1464.94.91.00.82.00.421.0N
563434224F452.3244.02.91.01.01.50.421.0N
672134225F502.0504.03.61.30.92.10.624.0N
742134227M484.7474.02.90.80.91.60.424.0N
867034229M432.6674.03.80.92.43.71.021.0N
975934230F323.6284.03.82.02.43.81.024.0N
IDNo_PationGenderAGEUreaCrHbA1cCholTGHDLLDLVLDLBMICLASS
990194454316F574.1709.35.33.31.01.41.329.0Y
9911954543f554.13413.95.41.61.63.10.733.0Y
992196454316M553.1398.55.02.51.92.90.727.0Y
993198454316M283.5618.54.51.91.12.60.837.0Y
994199454316M6910.31857.74.91.91.23.00.737.0Y
995200454317M7111.0977.07.51.71.21.80.630.0Y
996671876534M313.06012.34.12.20.72.415.437.2Y
99766987654M307.1816.74.11.11.22.48.127.4Y
9989924004M385.8596.75.32.01.62.914.040.5Y
99924824054M545.0676.93.81.71.13.00.733.0Y